Advances in Consumer Research
Issue 1 : 1454-1458
Original Article
From Cruisers to E-Bikes: A Market Segmentation Blueprint
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1
Associate Professor, Department of Management Studies, Dr. N.G.P. Institute of Technology, Coimbatore, Tamil Nadu, India – 641048
2
Assistant Professor (Senior Grade), PSG Institute of Management, PSG College of Technology, Coimbatore, Tamil Nadu, India – 641004
3
Associate Professor, School of Management, Sree Saraswathi Thyagaraja College, Pollachi, Tamil Nadu, India – 642207
4
Associate Professor, Department of Management Sciences, Hindusthan College of Engineering and Technology, Coimbatore, Tamil Nadu, India – 641008
5
Director, Rathinam Business School – Wisdom Campus, Gomangalampudur, Pollachi Taluk, Tamil Nadu 642107, India
6
Associate Professor, Dept of Computer Science and Engineering, School of Engineering and Technology, CGC University Mohali-140307, Punjab India
Abstract

The study uses Correspondence Analysis (CA) to find out what people think about different kinds of two-wheelers.  The study uses answers to questionnaires from 420 people in Coimbatore, India, to find out what kinds of motorcycles people like best: standard, sports, cruiser, electric, or off-road.  It does this by figuring out how the performance, safety, cost, brand reputation, and environmental sustainability of these bikes are all connected.   There are two main dimensions in the CA results: Dimension 1 (performance vs. rugged usefulness) and Dimension 2 (technology vs. traditional features).  The results show that while sports and cruiser bikes are linked to speed, style, and brand reputation, off-road motorcycles are linked to safety and cutting-edge technology.    People understand that while traditional bicycles are affordable and comfortable, electric bicycles are more environmentally friendly and incorporate new technology.

 The study shows that CA has a clear and understandable approach to market positioning, effectively dividing its clientele into multiple groups.    Future research should use bigger, multi-regional datasets and more precise prediction algorithms to mitigate problems such as sample bias and geographic constraints.    This study clarifies human behavior and offers useful information for creating new products and identifying the right markets in the quickly changing two-wheeler industry

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